Single Column Model representation of RICO shallow cumulus convection
Single Column Model representation of RICO shallow cumulus convection
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Single Column Model representation
of RICO shallow cumulus convection
A.Pier Siebesma and Louise Nuijens,KNMI, De Bilt
The Netherlands
Many thanks to: All the participants
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Main Question
• Are the single column model versions of GCM’s, ‘LAM’s and mesoscale models capable of representing realistic mean state when subjected to the best guess of the applied large scale forcings.
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The game to be played
tifdt
d
dt
d
dt
d
lsphystot
0
vuqwheret ,,,)0( 1. Start with the observed mean state:
2. Let the initial state evolve until it reaches steady state:
3. Evaluate the steady state with observations in all its aspects
with observations (both real and pseudo-obs (LES) ), i.e.
obsvs )(
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Two Flavours of the game
timeLSLS dt
d
dt
d
1. Use the mean LS-forcing of the suppressed period:
2. Use directly the the time-varying LS forcing for the whole suppressed period.
i.e. the composite case.
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Initial State = mean observed state
Remark: need to reconcile initial theta-profile
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Model Type Participant Institute
CAM3/GB GCM (Climate) C-L Lappen CSU (US)
UKMO GCM (NWP/Climate) B. Devendish UK Metoffice (UK)
JMA GCM (NWP/Climate) H. Kitagawa JMA (Japan)
HIRLAM/RACMO LAM (NWP/Climate) W. De Rooy KNMI (Netherlands)
GFDL GCM (Climate) C. Golaz GFDL (US)
RACMO/TKE LAM (Climate S. De Roode KNMI (Netherlands)
LMD GCM Climate) Levefbre LMD (France)
LaRC/UCLA LAM (Mesoscale) Anning Cheng NASA-LaRC (US)
ADHOC C-L Lappen CSU (US)
AROME LAM (Mesoscale) S. Malardel Meteo-France (France)
ECHAM GCM (Climate) R. Posselt ETH (Switzerland)
ARPEGE GCM (Climate) P. Marquet Meteo-France (France
ECMWF GCM (NWP) R. Neggers ECMWF (UK
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Model PBL Scheme Convection Cloud
CAM3/GB TKE (bretherton/grenier) MF (Hack) Prog l,
UKMOK-profile/expl entr. /moist(?)
MF (Gregory-Rowntree)Mb=0.03w*
Stat/RH_cr (Smith)
JMAK-profile/expl entr/moist.
MF (Arakawa-Schubert) Stat/RH_cr (Smith)
HIRLAM/RACMO
TKE/moist MF(Tiedtke89)New entr/detr, M=a w* closure
Stat, diagns from K and MF
GFDLK-profile/expl entr/moist(?)
MF (Rasch) l,c prognostic
RACMO/TKE TKE moist MF (Tiedtke(89) l,c,prognostic
LMD Ri-number MF (Emanuel) Stat
LaRC/UCLA3rd order pdf basedLarson/Golaz (2005)
3rd order pdf basedLarson/Golaz (2005)
3rd order pdf basedLarson/Golaz (2005)
ADHOCAssumed pdfhigh order MF
Assumed pdfhigh order MF
Assumed pdfhigh order MF
AROME TKE-moist MF (pbl/cu-updraft) Stat. diagnostic
ECHAMTKE-moist Tiedtke(89) Entr/detr
(Nordeng)Stat Tompkins 2002)
ARPEGETKE-moist
MFStat ,cloud coverL=prognostic
ECMWF K-profile (moist) MF (pbl/cu-updraft) Stat. diagnostic
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Different Building Blocks
Moist Convection
entr/detr
M_b , w_u
Extended in bl
Cloud scheme:
stat
progn
Precip?
precip?
microphysics
precip
PBL:K-profile
TKE
Higher order
ac, q
, q
acEstimating: ac,qlac,ql
on/off
• need increasingly more information from eachother
• demands more coherence between the schemes
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s
st qqtQ
Cloud cover
Bechtold and Cuijpers JAS 1995
Bechtold and Siebesma JAS 1999Wood (2002)
Statistical Cloud schemes
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Convective and turbulent transport
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Profiles after 24 hrs
Composite Case (High resolution)
80 levels ~ 100m resolution in cloud layer
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Initial “mean” state
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At least in general much better than with the previous Shallow cumulus case based on ARM
(profiles after ~10 hours
Lenderink et al. QJRMS 128 (2002)
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Cloud fraction Liquid water
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ARM
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Profiles after 72 hrs
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gradient in cloud layer to characterizethe state
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Are you all still with me?
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Time Series
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vv
vv
vv
v
CAM, UKMO : too low
GFDL,UKMO: too high
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ECHAM, Arpege: very noisy
(probably on average ok)
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ADHOC
CAM
Directly related to mean q, near surface
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On/off switching convection scheme
(ECHAM,UKMO)?
GFDL, LMD: unrealistically high
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Possible cause for intermittant behaviour:
w’qt’_scm
Mdz
dM
w’qt’ ~ M ( qt,u – qt)
Constant with height
Increasing with height
qz
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Most models don’t go to deep conv.
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GFDL
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ECHAM, UKMO, LMD : too low
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RACMO-TKE
ADHOC
Arpege
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RACMO-surface wind too low
CAM: ??? Surface wind + qt ok???
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ECHAM: very noisy
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JMA too cold near surface
ECHAM: very noisy
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Intermittant: Arpege, LMD, RACMO/TKE, GFDL
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Cause of noise might reside in TKE-scheme: Arpege, ECHAM,
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Most models do not have a very active
Diffusion scheme in cloud layer!!
Moist pbl-schemes are not overtaken the convection schemes.
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LES
ECMWF
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Evaporation of Rain in PBL
Relative precipitation ratio:
500
500
P
PPR srf
For LES in Cu : R ~ 0. (hardly rain evap)
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R
500
500
P
PPR srf
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We should clear up the obvious deficiencies
•Check LS Forcings: should we ask for it as required output?
• u,v –profiles : RACMO-TKE, ECMWF, UCLA-LaRC, ECHAM
•Ask for timeseries for u,v,q,T near surface to check surface fluxes and cloud base height off-line.
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Other remarks
• Noise in time-series related to TKE-scheme.
• on/off switching of convection related to mass flux profile.
• There seems to be no agreement on the precipitation evaporation efficiency.
• Most models don’t seem to trigger deep convection.
• Cloud cover, and liquid water profile 1st order problem, microphysics is a 2nd order problem (but might affect the mean state considerably!!)
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Some models behave remarkably well
• ECMWF, HIRLAM, AROME
• These models worked actively on shallow cumulus (but did not tune their parameterization on the present case)
• It seems that there are 3 crucial ingredients:
1. Good estimate of cloud base mass flux : M~ac w*
2. Good estimate of entrainment and detrainment
3. Good estimate of the variance of qt and l in the cloud layer in order to have a good estimate of cloud cover and liquid water.
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Required observational data
• Liquid water path (or even better profiles)
• cloud cover profiles (should be possible)
• .precipitation evaporation efficiency.
• Cloud base mass flux.
• Incloud properties., entrainment, detrainment mass flux (Hermann??)
• Variance of qt and theta (for cloud scheme purposes)
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Further Points:
• Proceed with the long run??
•Get the the RICO-sondes into the ECMWF/NCEP analysis in order to get better forcings?
•Should we do 3d-GCM RICO?
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Thank you